Strategic Placement of Branding Elements in Digital Marketing:

Insights from Eye-Tracking Data

Mohamed Basel Almourad

, Emad Bataineh

, Mohammed Hussein

and Zelal Wattar

College of Technological Innovation, Zayed University, Dubai, U.A.E.

College of Communication & Media Science, Zayed University, Dubai, U.A.E.

Keywords: Digital Marketing, Eye Tracking, Visual Attention, Consumer Behavior, Branding Elements.

Abstract: In today's media landscape, where consumers are overloaded with information and have shorter attention

spans, digital marketers face significant difficulty in grabbing and holding customers' attention. This research

examines how visual attention affects the processing of advertising stimuli. It does this by using eye-tracking

technology to determine where branding components should be placed in digital ads to maximize processing

efficiency and perceptual salience. The research shows that placing branding features strategically in the top

central part of the advertisement can greatly increase visual attention and subsequent recall by analyzing

fixation patterns and saccadic behavior. This result is consistent with well-known theories of visual attention,

such as the zoom lens model, which holds that processing and memory can be enhanced by focused visual

attention. The findings of the study provide marketers with important information on how to maximize the

impact of their advertising campaigns by using the principles of visual attention to convey clear, powerful

messages in a media landscape that is changing quickly.

1 INTRODUCTION

Digitalization has transformed both industrial and

consumer marketing within the last 20 years

(Herhausen et al., 2023). Digital marketing

encompasses all activities, organizations, and

procedures made possible by digital technologies for

the creation, communication, and delivery of services

to consumers (Homburg and Wielgos, 2022). This

includes e-commerce, mobile devices, smart

products, the Internet of Things (IoT), and artificial

intelligence (AI). As more and more people connect

to the internet, they are exposed to hundreds of

marketing messages every day, speeding up the field

of digital marketing (Helgesson and Stojkovic, 2023).

This presents a specific challenge for marketers in

developing a marketing segment that successfully

captures the consumer's attention and delivers their

message. As a result, their marketing efforts can be

overlooked in the vast ocean of digital impressions

that the average user is exposed to every day.

Measuring advertising effectiveness is one of the

highest importance to marketers as marketing spends

increase (Rodgers, 2024).

Eye tracking is a neuromarketing technique that

aids in both quantitative and qualitative analysis of

advertising materials to improve their efficacy (Lee

and Ahn, 2012). Marketers may assess visual

attention and use the results to create more engaging

ads by measuring it with the eye-tracking

methodology. With the use of eye-tracking

technology, marketers can measure visual attention

using metrics like fixation time, saccades, and gaze

patterns, providing them with accurate insights into

how consumers interact with commercials (Kim,

2024). These insights let advertisers know which

parts of an advertisement draw in viewers, which

parts are ignored, and how viewers interact with the

digital ads. Keyzer et al. (2023) showed that ads that

were successful in grabbing a lot of visual attention

were more likely to make people feel good about the

brand. This result emphasizes the significance of

creating visually appealing advertisements that draw

in and hold the attention of viewers. Marketers may

improve visual engagement and overall ad success by

optimizing their advertising strategy with the use of

eye tracking data. Marketers can improve ad efficacy

by using attention-grabbing cues like human faces

and subtle animations, optimizing visual hierarchy,

and simplifying messaging (Margariti et al., 2023).

These strategies enhance message retention and

engagement, which enhances consumer engagement.

Almourad, M. B., Bataineh, E., Hussein, M. and Wattar, Z.

Strategic Placement of Branding Elements in Digital Marketing: Insights from Eye-Tracking Data.

DOI: 10.5220/0013281500003929

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 27th International Conference on Enterprise Information Systems (ICEIS 2025) - Volume 2, pages 417-423

ISBN: 978-989-758-749-8; ISSN: 2184-4992

417

Even though visual attention and processing

fluency in digital marketing ads (Santoso et al., 2022)

(Margariti et al., 2023) have been studied extensively,

there are still a lot of significant gaps about how

consumers' goal-driven attention and inherent visual

behavior affect their expectations and perception of

branding aspects in advertisements. Prior research

frequently ignores how people's goals influence their

visual search behavior, especially when it comes to

finding brand identifiers, in favor of concentrating on

general attention patterns and the efficacy of different

ad components (Almourad et al., 2022; Toubia et al.,

2012; Glöckner et al., 2012). Additionally, little

research has been done on how consumers interpret

visually difficult or blurry ads, which exposes a gap

in knowledge of how to recognize brands in

inadequate viewing scenarios (Kay et al., 2023).

Although neuromarketing acknowledges processing

fluency as a critical component in improving

consumer perceptions of ads, most research so far has

focused on the advantages of readily identifiable and

intelligible stimuli (Genco et al., 2013). However,

little is known about how consumers' automatic gaze

patterns help them process information fluently,

particularly when it comes to recognizing branding

cues. Additionally, little research has been done on

how easily branding information is processed in

relation to pre-existing mental templates created by

repeated exposure to advertisements (Malodia et al,

2022). Addressing these gaps is important for

improving advertising effectiveness and aligning ad

designs with consumer needs.

This research attempts to identify customer

expectations regarding the positioning of branding

elements in advertising to make them easier to

understand and more consumer friendly. The present

study is based on previous research that has

demonstrated that people are more likely to pay

attention to stimuli that are thought to be pertinent to

their goals, highlighting the critical function of goal-

driven attention in cognitive processes (Toubia et al.,

2012; Glöckner et al., 2012). Individuals' goal-driven

visual attention style can be used to determine where

participants anticipate branding components to

appear in an advertisement. The study aims to

determine the visual locations consumers naturally

seek out while looking for brand identification cues

by assessing fixation time and fixation count on

certain areas of interest (AOIs) in a purposefully

blurred headphone advertisement. The results will

help to clarify how visual attention patterns and

expected brand element positioning in advertising

stimuli interact with one another.

Section 2 outlines the literature review, which

explores eye-tracking studies, consumer behavior,

and visual attention in advertising. Section 3 presents

the methodology details- participant demographics,

experimental setup, and data collection techniques.

Section 4 describes both qualitative and quantitative

results, contextualizing them with existing literature.

Section 5 concludes the findings of the research.

2 LITERATURE REVIEW

Eye-tracking methodologies have been instrumental

in elucidating the intricate relationship between

visual attention and consumer behavior within the

advertising domain. Previous studies have

highlighted the importance of visual attention metrics

as indicators of advertising effectiveness (Lee & Ahn,

2012; Casado-Aranda et al., 2023) (Almourad et al.,

2023) (

Almourad et al., 2022). Research has shown a

positive correlation between elevated levels of visual

attention and favorable consumer attitudes toward

advertisements (Keyzer et al., 2023). Another study

grounded in the AIDA model suggests that attention

is the first stage before a consumer takes action,

playing a crucial role in decision-making (Gahlot et

al., 2023). According to the AIDA framework,

elements like a logo's aesthetic appeal and saliency

influence consumer choices (Salarifar et al., 2020).

For instance, an experiment by Gahlot et al. (2023)

revealed that product and brand names received the

most visual attention, highlighting the significance of

capturing consumer focus early in the ad journey.

The ability to grab attention is critical as it triggers

the cognitive processing capacity of consumers,

transitioning them from the awareness phase to the

consideration phase (Almourad et al., 2023; Salarifar

et al., 2020; Felix & Hinck, 2016). Eye-tracking

serves as a powerful tool to optimize the 'attention'

stage of the consumer journey by linking visual

attention metrics with cognitive responses (Fidelis et

al., 2017; Riswanto et al., 2024). This study employs

eye-tracking to explore how optimizing the

placement of branding elements in advertisements

can enhance overall effectiveness. Branding elements

are essential for building long-term brand equity,

leading to increased purchase intent and consumer

trust (Foroudi, 2019; Hunt, 2019). More than just

symbols or text, a brand logo conveys meaning and

fosters a connection with consumers (Salarifar et al.,

2020).

ICEIS 2025 - 27th International Conference on Enterprise Information Systems

418

(a) (b)

Figure 1: (a) Stimulus AOI (b) Stimulus (unblurred).

Studies have shown that brand elements attract

significant visual attention, contributing to better

advertisement recognition (Boerman et al., 2015) (Ji

et al., 2023). Leveraging brand love, equity, and

loyalty, marketers can expedite consumer decision-

making processes by strategically optimizing brand

element salience and visual accessibility. Research by

Dogra & Kaushal (2023)concluded that attributes

perceived as most favorable by consumers tend to

attract greater attention, significantly influencing

their purchase decisions. Damaskinidis et al. (2018)

demonstrated that print advertisement layout

influences visual attention patterns, suggesting that

strategic manipulation of design elements can direct

consumer focus to enhance ad impact.

Due to the mere exposure effect, consumers

develop mental templates for where they expect

certain elements to appear in advertisements.

Marketers can leverage these templates to enhance

processing fluency by aligning ad layouts with

consumer preferences (Kotler, 2017). Research by

Bastrygina et al. (2024) found a robust correlation

between brand element prominence and subsequent

brand recall, suggesting that optimizing the visibility

and salience of brand elements can significantly

impact advertising effectiveness.

The concept of processing fluency in

neuromarketing explains the ease with which stimuli

are identifiable and understandable, making them

more appealing to the brain (Genco et al., 2013) (Lin

et al., 2024). High processing fluency is linked to

perceptions of truth, persuasiveness, and likability.

However, there is a gap in understanding how natural

consumer visual behavior influences processing

fluency, particularly concerning the identification of

branding elements. Existing studies focus primarily

on the benefits of easily processed stimuli but often

overlook consumers' instinctive gaze patterns that

drive processing fluency (Affonso & Janiszewski,

2023). This study aims to fill that gap by examining

where consumers are naturally drawn when

identifying branding elements in ads, leveraging

fixation duration and fixation count metrics.

The proposed research aims to fill in current

knowledge gaps in consumer visual behavior and how

it affects the efficacy of advertising by concentrating

on processing fluency. Prior research has thoroughly

examined visual attention statistics and their impact

on the efficacy of advertisements; however, little is

known about how consumers' goal-driven attention

and natural gaze patterns facilitate the processing of

branding aspects in ads. This study intends to further

our knowledge of the fundamental cognitive

processes that underpin processing fluency by

examining how consumers naturally concentrate on

branding cues, particularly in a variety of visual

contexts. This study not only fills in the knowledge

gaps regarding how mental templates created by

repeated exposure affect brand recognition, but it also

offers practical advice on how to organize

advertisements to increase consumer engagement.

Strategic Placement of Branding Elements in Digital Marketing: Insights from Eye-Tracking Data

419

3 METHODOLOGY

Neuroscientific methodology is used in the proposed

study to address the research goal. Participants used a

screen-based eye-tracking device to record visual

metrics, which allowed behavioral and implicit data

to be investigated. The use of an eye tracker reduced

the possibility of response bias or confabulation

mistakes, ensuring the neutrality of data gathering.

All individuals gave their informed consent before

participating, and the study complied with ethical

standards. The experiment was conducted in Zayed

University, Dubai's Human-Computer Interaction

(HCI) LabThe study involved 83 students in total, 34

of whom were male and 49 of whom were female. To

ensure uniform exposure time across participants,

each participant looked at and evaluated the stimuli

images for a set amount of time—five seconds per

image. After providing informed consent,

participants were given on-screen instructions

outlining the task requirements. The experimental

stimuli were then shown for five seconds. After

viewing the stimuli, participants were asked to

answer a brief post-experiment questionnaire. This

was done to preserve the experiment's legitimacy and

ecological validity, even though the questionnaire's

content had nothing to do with the major goals of the

study. The current study does not address the

questionnaire's results. A Tobii eye tracker was used

to capture the participants' visual behavior, which was

then examined using the Tobii Studio platform.

A blurred image of a fictional headphone

advertisement was used as a stimulus for the

experiment (see Figure 1(a)). Participants were given

the task of finding the brand name/logo before being

presented with the stimuli. The stimuli were designed

to elicit heightened visual engagement and compel

participants to actively search for the branded

elements. The Areas of Interest (AOIs) were

positioned according to the different areas the

branding elements are positioned in an advertisement.

Figure 1(b) illustrates the unblurred image used for

experiment.

4 RESULTS AND DISCUSSION

The results section presents the study's findings based

on qualitative and quantitative analyses of eye-tracking

data. Qualitative insights include heat maps and eye

gaze plots, while quantitative analysis examines key

metrics such as fixation count, fixation duration,

percentage fixated, and time to first fixation (TTFF).

4.1 Fixation Duration

This study employed total fixation duration within

predefined AOIs as a key visual metric. The rationale

behind this choice is the established link between

extended fixation duration and heightened cognitive

processing directed toward a specific location. In this

research, increased fixation duration on an AOI

indicates that participants are engaging in more

focused visual exploration in that area. This extended

dwell time suggests that participants may be

associating a higher likelihood of encountering the

brand name or logo within that specific AOI. Several

studies have explored task-specific effects on

attention (Gahlot, Wu, & Reingold, 2010; Glöckner

et al., 2012; Toubia, de Jong, Stieger, & Füller, 2012),

with results showing that people tend to pay more

attention to goal-relevant stimuli. As shown in Figure

2, participants fixated the longest on the top central

area of the stimuli when tasked with identifying the

brand name or logo. The high fixation duration in this

area indicated that participants expected to find the

brand name/logo there. The second most fixated

location was the top left area, followed by the top

right area of the stimuli, which aligns with the

findings of Peker et al. (2021). Their eye-tracking

study found that central areas of banner

advertisements were noticed first, followed by the left

side, and then the right side, reflecting the Western

style of reading and writing.

Figure 2: Sum of Fixation Duration when participants were

tasked to identify the brand name/logo in stimuli 1.

4.2 Fixation Count

Fixation Count is an eye-tracking metric that helps

gauge repetitive eye movement behavior. It refers to

the number of times a participant fixates on an Area

of Interest (AOI) (Tobii Studio User’s Manual

Version 3.4.6, 2015). Both Fixation Duration and

Fixation Count were considered for this study, as they

are widely recognized as effective indicators of visual

attention (Gahlot et al., 2023; Wedel & Pieters, 2006).

ICEIS 2025 - 27th International Conference on Enterprise Information Systems

420

Figure 3 illustrates the sum of the Fixation Count

when participants were tasked with identifying the

brand name/logo in Stimuli 1. The Fixation Count

was highest in the top central area of the stimuli.

Participants repeatedly fixated on this area of the

advertisement, indicating that they expected the

branding elements to be located there.

Figure 3: Sum of Fixation Count when participants were

tasked to identify the brand name/logo in stimuli 1.

4.3 Percentage Fixated Mean

Percentage fixated, as defined in the Tobii Studio

User's Manual (Version 3.4.6, 2015), quantifies the

frequency with which participants' visual attention is

directed towards a specific AOI during a given

recording session. It is calculated as the ratio of

fixations within the AOI to the total number of

fixations recorded. Figure 4 shows the mean

percentage fixated of visual attention of participants.

The Top-Center AOI had the highest mean

percentage fixated, indicating that most participants

fixated on this AOI at least once. The high mean value

suggests that most participants' fixations were

concentrated in this area, implying that they

anticipated the brand name/logo to be located there.

Figure 4: Percentage Fixated Mean.

4.4 Time to First Fixation Mean

Time to First Fixation (TTFF) is a fundamental eye-

tracking metric that quantifies the temporal interval

between stimulus onset and the initiation of the

participant's first fixation within a designated AOI

(Tobii Studio User’s Manual Version 3.4.6, 2015).

Figure 5 illustrates the mean TTFF across the AOIs.

Given the alignment of the Mid-Left AOI with the

participant's natural gaze trajectory, it is unsurprising

that this region emerged as the initial focal point for

visual attention, as evidenced by the comparatively

lower TTFF values recorded for both the Mid-Left

and Mid-Right AOIs. Participants were quickly

drawn to the left side of the stimuli and expected the

brand name/logo to appear in the top-left area. This is

further supported by the low TTFF and high fixation

duration (Figure 2) observed in the Top-Left AOI of

the stimuli. These results are consistent with the

findings of Peker et al (2021), who observed that

participants noticed the left area first.

Figure 5: Time to First Fixation Mean – AOIs.

4.5 Time to First Fixation Mean

Figure 6: Total Visit Duration Mean – AOIs.

Figure 6 illustrates the average total visit duration of

all participants in each AOI (Tobii Studio User’s

Strategic Placement of Branding Elements in Digital Marketing: Insights from Eye-Tracking Data

421

Manual Version 3.4.6, 2015). The results closely

align with those of the Fixation Duration metrics

(Figure 2), with the Top-Center area being the most

visually visited, followed by the Top-Right and Top-

Left areas. These findings suggest that participants

anticipated the brand name/logo elements to be

positioned at the top of the advertisement. The high

total visit duration and Fixation Duration (Figure 2)

in the top areas further indicate that participants are

primed to focus on the top of an advertisement when

searching for branding elements.

5 CONCLUSIONS

In conclusion, this study provides empirical evidence

supporting the strategic placement of branding

elements within advertisements. The findings

emphasize the importance of the top-central region as

a prime location for capturing and sustaining visual

attention. By aligning with natural viewing patterns

and cognitive expectations, marketers can enhance

the visibility and memorability of branding elements

by placing them in the top-central area of an

advertisement. Quantitative analysis of participants'

eye-tracking data revealed a high density of fixations

and prolonged gaze duration within this region. The

results highlight the potential of eye-tracking

technology to provide valuable insights into

consumer behavior and inform the development of

more effective advertising strategies.

However, there are several limitations to this

study. First, the findings' generalizability may be

limited by the sample size and participant

demographics, which might not accurately reflect the

diversity of consumer groups. Second, the study only

looked at static ads, ignoring the effect of branding

placement in interactive or dynamic media. Future

research could explore the role of individual

differences, such as visual acuity and attention span,

in modulating the effectiveness of various ad

placements, providing further insights into the

complex interplay between visual attention and

advertising effectiveness. Additionally, to account for

cultural, age, and socioeconomic variations that may

affect visual attention and advertising efficacy, future

studies should build on these findings by integrating

additional demographic groups. Furthermore,

investigating branding placement across platforms—

such as interactive environments and dynamic digital

media—may provide a more profound understanding

of cross-platform consumer behavior and enhance

generalizable results across various advertising

formats.

REFERENCES

Affonso, F. M., & Janiszewski, C. (2023). Marketing by

design: The influence of perceptual structure on brand

performance. Journal of Marketing, 87(5), 736-754.

Almourad, M. B., Bataineh, E., & Wattar, Z. (2022,

September). Dynamic Facial Recognition in Autism:

The Case of Happy Face Expression. In 2022

International Conference on Theoretical and Applied

Computer Science and Engineering (ICTASCE) (pp.

99-104). IEEE.

Almourad, M. B., Bataineh, E., & Wattar, Z. (2022).

Exploring usability challenges of e-services in

university academic portals: An eye-tracking analysis

of participant’s navigation and searching behavior. In

International Conference on Communication and

Network Technology (pp. 84–96). Springer

International Publishing.

Almourad, M. B., Bataineh, E., & Wattar, Z. (2023).

Responses to sad emotion in autistic and normal

developing children: Is there a difference? Journal of

Image and Graphics (United Kingdom), 11(1), 40.

EJournal Publishing.

Almourad, M. B., Bataineh, E., Hussain, M., & Wattar, Z.

(2023). Usability assessment of a university academic

portal using eye tracking technology. Procedia

Computer Science, 220, 323-330.

Bastrygina, T., Lim, W. M., Jopp, R., & Weissmann, M. A.

(2024). Unraveling the power of social media

influencers: Qualitative insights into the role of

Instagram influencers in the hospitality and tourism

industry. Journal of Hospitality and Tourism

Management, 58, 214-243.

Boerman, S. C., Van Reijmersdal, E. A., & Neijens, P. C.

(2015). Using eye tracking to understand the effects of

brand placement disclosure types in television

programs. Journal of Advertising, 44(3), 196-207.

Casado-Aranda, L. A., Sánchez-Fernández, J., & Ibáñez-

Zapata, J. Á. (2023). Evaluating communication

effectiveness through eye tracking: Benefits, state of

the art, and unresolved questions. International Journal

of Business Communication, 60(1), 24-61.

Damaskinidis, G., Kourdis, E., Zantides, E., & Sykioti, E.

(2018). Eye-tracking the semiotic effects of layout on

viewing print advertisements. Public Journal of

Semiotics, 8(1), 46-66.

De Keyzer, F., Dens, N., & De Pelsmacker, P. (2023). The

processing of native advertising compared to banner

advertising: an eye-tracking experiment. Electronic

Commerce Research, 23(3), 1921-1940.

Dogra, P., & Kaushal, A. (2023). The impact of Digital

Marketing and Promotional Strategies on attitude and

purchase intention towards financial products and

service: A Case of emerging economy. Journal of

Marketing Communications, 29(4), 403-430.

Felix, R., & Hinck, W. (2015, October). Attention to Print

Advertising: An Eye Tracking Study in the Context of

Airline Advertisements. In Thriving in a New World

Economy: Proceedings of the 2012 World Marketing

Congress/Cultural Perspectives in Marketing

ICEIS 2025 - 27th International Conference on Enterprise Information Systems

422

Conference (pp. 252-255). Cham: Springer

International Publishing.

Fidelis, B. T., Oliveira, J. H. C., Giraldi, J. D. M. E., &

Santos, R. O. J. (2017). Sexual appeal in print media

advertising: effects on brand recall and fixation time.

Research Journal of Textile and Apparel, 21(1), 42-58.

Foroudi, P. (2019). Influence of brand signature, brand

awareness, brand attitude, brand reputation on hotel

industry’s brand performance. International journal of

hospitality management, 76, 271-285.

Gahlot, P., Thapa, S. B., Suryavanshi, K., Gandhi, A., &

Raman, R. (2023). Effect of Timing Sequence of Brand

Promotion in an Online Video-an Eye-Tracking Study.

J of Comp Sc, 19(1), 99-111.

Genco, S. J., Pohlmann, A. P., & Steidl, P. (2013).

Neuromarketing for dummies. John Wiley & Sons.

Glöckner, A., Fiedler, S., Hochman, G., Ayal, S., & Hilbig,

B. E. (2012). Processing differences between

descriptions and experience: A comparative analysis

using eye-tracking and physiological measures.

Frontiers in psychology, 3, 173.

Helgesson, J., & Stojkovic, K. (2023). Consumer Attention

Marketing Strategies: How do companies’ perception

of online consumers’ attention span affect their digital

marketing strategies?.

Herhausen, D., Miočević, D., Morgan, R. E., & Kleijnen,

M. H. (2020). The digital marketing capabilities gap.

Industrial Marketing Management, 90, 276-290.

Homburg, C., & Wielgos, D. M. (2022). The value

relevance of digital marketing capabilities to firm

performance. Journal of the Academy of Marketing

Science, 50(4), 666-688.

Hunt, S. D. (2019). The ethics of branding, customer-brand

relationships, brand-equity strategy, and branding as a

societal institution. Journal of Business Research, 95,

408-416.

Ji, M., Liu, Y., & Chen, X. (2023). An eye-tracking study

on the role of attractiveness on consumers’ purchase

intentions in e-commerce live streaming. Electronic

Commerce Research, 1-36.

Kay, S., Mulcahy, R., Sutherland, K., & Lawley, M. (2023).

Disclosure, content cues, emotions and behavioural

engagement in social media influencer marketing: an

exploratory multi-stakeholder perspective. Journal of

Marketing Management, 39(7-8), 550-584.

Kim, N. (2024). Capturing Initial Gaze Attraction in

Branded Spaces Through VR Eye-Tracking

Technology. International Journal of Human–

Computer Interaction, 1-14.

Kotler, P. (2017). Consumer neuroscience. Mit Press.

Lee, J., & Ahn, J. H. (2012). Attention to banner ads and

their effectiveness: An eye-tracking approach.

International Journal of Electronic Commerce, 17(1),

119-137.

Lin, M. H., Jones, W., & Childers, T. L. (2024).

Neuromarketing as a scale validation tool:

Understanding individual differences based on the style

of processing scale in affective judgements. Journal of

Consumer Behaviour, 23(1), 171-185.

Malodia, S., Dhir, A., Bilgihan, A., Sinha, P., & Tikoo, T.

(2022). Meme marketing: How can marketers drive

better engagement using viral memes?. Psychology &

Marketing, 39(9), 1775-1801.

Margariti, K., Hatzithomas, L., & Boutsouki, C. (2023).

Implementing eye tracking technology in experimental

design studies in food and beverage advertising. In

Consumer research methods in food science (pp. 293-

311). New York, NY: Springer US.

Peker, S., Menekse Dalveren, G. G., & İnal, Y. (2021). The

effects of the content elements of online banner ads on

visual attention: evidence from an-eye-tracking study.

Future Internet, 13(1), 18.

Pieters, R., Warlop, L., & Wedel, M. (2002). Breaking

through the clutter: Benefits of advertisement

originality and familiarity for brand attention and

memory. Management science, 48(6), 765-781.

Riswanto, A. L., Ha, S., Lee, S., & Kwon, M. (2024).

Online Reviews Meet Visual Attention: A Study on

Consumer Patterns in Advertising, Analyzing

Customer Satisfaction, Visual Engagement, and

Purchase Intention. Journal of Theoretical and Applied

Electronic Commerce Research, 19(4), 3102-3122.

Rodgers, E. (2024) ‘Advertising Effectiveness: Reasons to

Measure & How To’, drive research, https://www.drive

research.com/market-research-company-blog/advertise

ng-effectivenesssurveys/#:~:text=Measuring%20ad%2

0effectiveness%20helps%20you,to%20increase%20ret

urn%20on%20investment.

Salarifar, M., Vakil Alroaia, Y., Danaei, A., Riazi, G. H., &

De Moura Engracia Giraldi, J. (2020). Visual attention

to the Logos of Popular and Unknown Brands: An Eye-

tracking Study during Decision-making. Journal of

Information Technology Management, 12(4), 200-214.

Santoso, I., Wright, M. J., Trinh, G., & Avis, M. (2022).

Mind the attention gap: how does digital advertising

impact choice under low attention?. European Journal

of Marketing, 56(2), 442-466.

Tobii Studio User’s Manual Version 3.4.6, (2015), Tobii

pro, Tobii AB (publ).

Toubia, O., De Jong, M. G., Stieger, D., & Füller, J. (2012).

Measuring consumer preferences using conjoint poker.

Marketing Science, 31(1), 138-156.

Vu, T. M. H., Tu, V. P., & Duerrschmid, K. (2018). Eye-

tracking test design influences the relationship between

gazing behaviour and evaluation decision. Die

Bodenkultur: Journal of Land Management, Food and

Environment, 68(4), 261-270.

Wedel, M., & Pieters, R. (2006). Eye tracking for visual

marketing. Foundations and Trends® in Marketing,

1(4), 231-320.

Strategic Placement of Branding Elements in Digital Marketing: Insights from Eye-Tracking Data

423